Dissertations in Logic Programming
An Open Invitation:
The column on dissertations in Logic Programming is aimed at widely publicizing recent dissertations (and even MS Theses) deadling with topics relevant to Logic Programming. This is an outstanding opportunity to
- Shine a spotlight on the new generation of logic programmers
- Demonstrate that the field of logic programming is alive with new "blood"
- Provide to our graduating students with an additional opportunity to get themselves known to the logic programming community at large
- Perhaps facilitate the creation of contacts with potential employers
Enrico
Pattern Based Inductive Logic
Programming
Chongbing Liu
New Mexico State University
USA
Chongbing Liu
New Mexico State University
USA
Efficiency
and effectiveness are two of the most important properties of Inductive
Logic Programming (ILP) systems. This thesis presents a new efficient
and effective formalism for ILP, called Pattern Based Inductive Logic
Programming (PB-ILP). This formalism is based on the concepts of
instances and patterns. While an instance represents a specific case of
the concept to be learned, a pattern represents the structural
information of a set of instances with the same properties.
In this formalism, the instances and patterns are first obtained, and then a theory about the target concept is constructed from the obtained patterns. While inducing rules from the obtained patterns, we employ a new search algorithm, where new search heuristics are used, which require only negative coverage information. This search algorithm significantly reduces the total coverage test cost and provides new opportunities for learning different theories when multiple solutions exist.
The PB-ILP formalism has been implemented in a new ILP system—the Pattern-based Induction Engine (PIE). This system is shown to be more efficient and effective than the state of the art ILP system, ALEPH, over a significant set of benchmarks.
The recent advent of the Semantic Web has increased the interest in the use of formal Knowledge Representation (KR) languages in order to allow automated processing of, and reasoning with, information on the Web. A prominent KR paradigm on the Web is the family of Description Logics (DLs), which are subsets of classical First-Order Logic (FOL); the W3C Web Ontology Language OWL, and especially its sub-language OWL DL, is strongly related to DL. Another KR paradigm receiving widespread attention in this context is that of rules, in the form of Logic Programming (LP); it is used in, for example, Semantic Web policies and Semantic Web services. Meta-modeling is a feature many deem useful, or even necessary, for the Semantic Web, and is present in the Semantic Web languages RDFS and OWL Full. F-Logic is a formalism that allows meta-modeling, in the spirit of RDFS, but is in many respects more in line with standard KR and database languages.
It is the goal of this thesis to combine DL ontologies, LP rules, and meta-modeling capabilities in a single unifying language framework for the Semantic Web.
We propose WSML as such a unifying framework. The WSML-DL sub-language corresponds to the DL SHIQ, and the WSML-Rule sub-language corresponds to LP with negation, extended with F-Logic-based meta-modeling. Interoperation between these sub-languages can be achieved through a common subset (WSML-Core) or a common superset (WSML-Full). In the technical design of the subset and the superset we face two major challenges: the combination of DL-style and F-Logic-style ontology modeling, and the interoperation between ontologies based on classical FOL (e.g., DL) and rules based on nonmonotonic LP.
To address the relationship with the basic Semantic Web language RDF(S) we define embeddings of the language in F-Logic, and demonstrating how standard KR techniques can be used for reasoning with and extending RDFS. Finally, we consider intensional OWL, a variant of OWL Full more suitable for rule-based processing and extension.
In this formalism, the instances and patterns are first obtained, and then a theory about the target concept is constructed from the obtained patterns. While inducing rules from the obtained patterns, we employ a new search algorithm, where new search heuristics are used, which require only negative coverage information. This search algorithm significantly reduces the total coverage test cost and provides new opportunities for learning different theories when multiple solutions exist.
The PB-ILP formalism has been implemented in a new ILP system—the Pattern-based Induction Engine (PIE). This system is shown to be more efficient and effective than the state of the art ILP system, ALEPH, over a significant set of benchmarks.
Semantic Web Language Layering
with Ontologies, Rules, and Meta-Modeling
Jos de Bruijn
University of Innsbruck
Austria
Jos de Bruijn
University of Innsbruck
Austria
The recent advent of the Semantic Web has increased the interest in the use of formal Knowledge Representation (KR) languages in order to allow automated processing of, and reasoning with, information on the Web. A prominent KR paradigm on the Web is the family of Description Logics (DLs), which are subsets of classical First-Order Logic (FOL); the W3C Web Ontology Language OWL, and especially its sub-language OWL DL, is strongly related to DL. Another KR paradigm receiving widespread attention in this context is that of rules, in the form of Logic Programming (LP); it is used in, for example, Semantic Web policies and Semantic Web services. Meta-modeling is a feature many deem useful, or even necessary, for the Semantic Web, and is present in the Semantic Web languages RDFS and OWL Full. F-Logic is a formalism that allows meta-modeling, in the spirit of RDFS, but is in many respects more in line with standard KR and database languages.
It is the goal of this thesis to combine DL ontologies, LP rules, and meta-modeling capabilities in a single unifying language framework for the Semantic Web.
We propose WSML as such a unifying framework. The WSML-DL sub-language corresponds to the DL SHIQ, and the WSML-Rule sub-language corresponds to LP with negation, extended with F-Logic-based meta-modeling. Interoperation between these sub-languages can be achieved through a common subset (WSML-Core) or a common superset (WSML-Full). In the technical design of the subset and the superset we face two major challenges: the combination of DL-style and F-Logic-style ontology modeling, and the interoperation between ontologies based on classical FOL (e.g., DL) and rules based on nonmonotonic LP.
- We address the combination of DL-style and F-Logic-style ontology modeling by defining a straightforward translation from FOL to F-Logic, and show that this translation preserves validity for the class of cardinal formulas. We exhibit a novel class of cardinal formulas that includes the DL SHIQ. We use this result to show that the translation from SHIQ to F-Logic preserves entailment.
- We address the interoperation between FOL and LP by analyzing a number of representational issues that occur when combining FOL theories with logic programs, and present a novel approach to combining FOL and LP using first-order autoepistemic logic, an expressive nonmonotonic logic, as a unifying formalism that features a tight integration between the rules and the ontology. We show how different embeddings of logic programs lead to difference semantics for the combination.
To address the relationship with the basic Semantic Web language RDF(S) we define embeddings of the language in F-Logic, and demonstrating how standard KR techniques can be used for reasoning with and extending RDFS. Finally, we consider intensional OWL, a variant of OWL Full more suitable for rule-based processing and extension.